A Heuristic Traffic Evacuation Approach for Emergency Vehicles in Mixed Traffic Flow in Urban Areas

Abstract

Emergency vehicles (EVs) play a pivotal role in promptly responding to time-sensitive situations, such as traffic accidents, medical emergencies, and fires in urban areas. Conventional traffic control methodologies primarily minimize EV travel time by granting them the highest road-use priority, potentially leading to delays for other nearby traffic participants and disrupting the flow of normal traffic. Connected and automated vehicles (CAVs) can help achieve the optimization of the transportation system. Thus, this work proposes an innovative heuristic approach aimed at reducing both EV travel time and the adverse impact on normal traffic by detouring CAVs adjacent to an EV’s path. Specifically, it periodically and dynamically establishes a subnet for each road segment along the EV route. Within the subnetwork, CAVs would be prohibited from utilizing the EV path, thus expediting emergency service delivery. Furthermore, the approach incorporates mechanisms for rerouting CAVs to alleviate congestion. Comprehensive experimental results demonstrate that compared with the heuristic traffic clear-out coordination algorithm (HTCC), the proposed approach reduces the negative impact of EVs on normal traffic by 8.5%, 8.1%, 6.2%, and 7.4%, respectively, across four metrics, including average travel time, average time loss, average waiting time, and average number of stops for EVs and regular vehicles. In addition, sensitivity analyses are conducted to evaluate the impact of penetration rates of CAVs and varying traffic densities. The results indicate that under different penetration rates of CAVs, the proposed approach outperforms the benchmark methods across all four metrics, with advantages becoming more pronounced at higher penetration rates of CAVs. Compared with the noncontrol strategy, our approach and HTCC reduce the response time of EVs by 73.1% and 72.6%, 76.3% and 74.7%, and 77.6% and 74.7% in low, medium, and high traffic densities, respectively. Our approach demonstrates overall superior performance to HTCC, particularly under high traffic densities. This work and its findings enhance the deployment of smart EV management systems.